Overview

Dataset statistics

Number of variables16
Number of observations50
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.4 KiB
Average record size in memory130.6 B

Variable types

Text3
Numeric11
Categorical2

Alerts

acousticness is highly overall correlated with energyHigh correlation
danceability is highly overall correlated with speechiness and 1 other fieldsHigh correlation
energy is highly overall correlated with acousticness and 1 other fieldsHigh correlation
loudness is highly overall correlated with energyHigh correlation
speechiness is highly overall correlated with danceabilityHigh correlation
valence is highly overall correlated with danceabilityHigh correlation
time_signature is highly imbalanced (82.2%)Imbalance
Track_ID has unique valuesUnique
Track_Name has unique valuesUnique
loudness has unique valuesUnique
acousticness has unique valuesUnique
valence has unique valuesUnique
tempo has unique valuesUnique
duration_ms has unique valuesUnique
key has 2 (4.0%) zerosZeros
instrumentalness has 22 (44.0%) zerosZeros

Reproduction

Analysis started2023-11-23 08:39:02.141086
Analysis finished2023-11-23 08:39:21.166592
Duration19.03 seconds
Software versionydata-profiling vv4.6.2
Download configurationconfig.json

Variables

Track_ID
Text

UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2023-11-23T11:39:21.394502image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters1100
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)100.0%

Sample

1st row1KGvrw4RWClq7yt42sGG1v
2nd row3auN910HHNYykQo8i9q5J1
3rd row0JU2QfhTrgFsVq5yQ4YQlu
4th row2huuuyJHQ6KK6qzwvUlG3c
5th row4rbXJipz3CaSQwtG3xXzzT
ValueCountFrequency (%)
1kgvrw4rwclq7yt42sgg1v 1
 
2.0%
6thhauamwuwlegpavq1jlx 1
 
2.0%
42bov2fgsztynqmaeoajb6 1
 
2.0%
0ju2qfhtrgfsvq5yq4yqlu 1
 
2.0%
2huuuyjhq6kk6qzwvulg3c 1
 
2.0%
4rbxjipz3casqwtg3xxzzt 1
 
2.0%
1piyttonn0m4kfjwlfjhny 1
 
2.0%
1widx6xw7b01snnjoamril 1
 
2.0%
6kvf06ethdcrvyknmy32to 1
 
2.0%
3wtoacctaklbwmzbckrshr 1
 
2.0%
Other values (40) 40
80.0%
2023-11-23T11:39:21.897839image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 30
 
2.7%
4 29
 
2.6%
1 27
 
2.5%
2 27
 
2.5%
3 27
 
2.5%
w 25
 
2.3%
H 24
 
2.2%
z 24
 
2.2%
0 24
 
2.2%
U 23
 
2.1%
Other values (52) 840
76.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 446
40.5%
Uppercase Letter 425
38.6%
Decimal Number 229
20.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w 25
 
5.6%
z 24
 
5.4%
n 22
 
4.9%
u 21
 
4.7%
r 21
 
4.7%
m 20
 
4.5%
k 20
 
4.5%
v 20
 
4.5%
i 19
 
4.3%
q 19
 
4.3%
Other values (16) 235
52.7%
Uppercase Letter
ValueCountFrequency (%)
H 24
 
5.6%
U 23
 
5.4%
G 23
 
5.4%
Z 22
 
5.2%
Q 21
 
4.9%
S 20
 
4.7%
L 19
 
4.5%
T 19
 
4.5%
A 18
 
4.2%
V 17
 
4.0%
Other values (16) 219
51.5%
Decimal Number
ValueCountFrequency (%)
6 30
13.1%
4 29
12.7%
1 27
11.8%
2 27
11.8%
3 27
11.8%
0 24
10.5%
7 21
9.2%
8 18
7.9%
5 14
6.1%
9 12
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 871
79.2%
Common 229
 
20.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
w 25
 
2.9%
H 24
 
2.8%
z 24
 
2.8%
U 23
 
2.6%
G 23
 
2.6%
Z 22
 
2.5%
n 22
 
2.5%
u 21
 
2.4%
r 21
 
2.4%
Q 21
 
2.4%
Other values (42) 645
74.1%
Common
ValueCountFrequency (%)
6 30
13.1%
4 29
12.7%
1 27
11.8%
2 27
11.8%
3 27
11.8%
0 24
10.5%
7 21
9.2%
8 18
7.9%
5 14
6.1%
9 12
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 30
 
2.7%
4 29
 
2.6%
1 27
 
2.5%
2 27
 
2.5%
3 27
 
2.5%
w 25
 
2.3%
H 24
 
2.2%
z 24
 
2.2%
0 24
 
2.2%
U 23
 
2.1%
Other values (52) 840
76.4%

Track_Name
Text

UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2023-11-23T11:39:22.272036image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length28
Median length18
Mean length11.88
Min length2

Characters and Unicode

Total characters594
Distinct characters60
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)100.0%

Sample

1st rowMargiela
2nd rowAteşe Düştüm
3rd rowAğlama ben ağlarım
4th rowİlle de Sen
5th rowFark Ettim
ValueCountFrequency (%)
sen 2
 
2.0%
olmuÅŸum 2
 
2.0%
ben 2
 
2.0%
de 2
 
2.0%
mı 2
 
2.0%
bir 2
 
2.0%
var 2
 
2.0%
mini 1
 
1.0%
can 1
 
1.0%
bellydancing 1
 
1.0%
Other values (83) 83
83.0%
2023-11-23T11:39:22.853672image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 63
 
10.6%
50
 
8.4%
e 40
 
6.7%
l 34
 
5.7%
n 30
 
5.1%
r 26
 
4.4%
m 24
 
4.0%
i 23
 
3.9%
ı 20
 
3.4%
A 17
 
2.9%
Other values (50) 267
44.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 387
65.2%
Uppercase Letter 145
 
24.4%
Space Separator 50
 
8.4%
Decimal Number 4
 
0.7%
Other Punctuation 4
 
0.7%
Close Punctuation 2
 
0.3%
Open Punctuation 2
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 63
16.3%
e 40
10.3%
l 34
 
8.8%
n 30
 
7.8%
r 26
 
6.7%
m 24
 
6.2%
i 23
 
5.9%
ı 20
 
5.2%
t 16
 
4.1%
u 14
 
3.6%
Other values (18) 97
25.1%
Uppercase Letter
ValueCountFrequency (%)
A 17
 
11.7%
B 13
 
9.0%
R 10
 
6.9%
Y 9
 
6.2%
D 9
 
6.2%
C 8
 
5.5%
S 8
 
5.5%
T 8
 
5.5%
O 8
 
5.5%
I 7
 
4.8%
Other values (13) 48
33.1%
Decimal Number
ValueCountFrequency (%)
0 2
50.0%
2 1
25.0%
1 1
25.0%
Other Punctuation
ValueCountFrequency (%)
. 2
50.0%
& 1
25.0%
' 1
25.0%
Space Separator
ValueCountFrequency (%)
50
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 532
89.6%
Common 62
 
10.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 63
 
11.8%
e 40
 
7.5%
l 34
 
6.4%
n 30
 
5.6%
r 26
 
4.9%
m 24
 
4.5%
i 23
 
4.3%
ı 20
 
3.8%
A 17
 
3.2%
t 16
 
3.0%
Other values (41) 239
44.9%
Common
ValueCountFrequency (%)
50
80.6%
) 2
 
3.2%
0 2
 
3.2%
( 2
 
3.2%
. 2
 
3.2%
2 1
 
1.6%
1 1
 
1.6%
& 1
 
1.6%
' 1
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 538
90.6%
None 56
 
9.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 63
 
11.7%
50
 
9.3%
e 40
 
7.4%
l 34
 
6.3%
n 30
 
5.6%
r 26
 
4.8%
m 24
 
4.5%
i 23
 
4.3%
A 17
 
3.2%
t 16
 
3.0%
Other values (41) 215
40.0%
None
ValueCountFrequency (%)
ı 20
35.7%
ü 10
17.9%
ÅŸ 9
16.1%
ÄŸ 9
16.1%
Åž 2
 
3.6%
İ 2
 
3.6%
ç 2
 
3.6%
ö 1
 
1.8%
Ç 1
 
1.8%
Distinct46
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2023-11-23T11:39:23.210488image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length37
Median length25
Mean length16.26
Min length8

Characters and Unicode

Total characters813
Distinct characters68
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)88.0%

Sample

1st row['Ezhel']
2nd row['Mert Demir']
3rd row['Canozan']
4th row['Muti', 'Azer Bülbül']
5th row['Semicenk']
ValueCountFrequency (%)
semicenk 5
 
5.1%
blok3 3
 
3.1%
bülbül 3
 
3.1%
azer 3
 
3.1%
dedublüman 2
 
2.0%
melike 2
 
2.0%
motive 2
 
2.0%
derya 2
 
2.0%
bulut 2
 
2.0%
muti 2
 
2.0%
Other values (69) 72
73.5%
2023-11-23T11:39:23.868276image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 126
 
15.5%
[ 50
 
6.2%
e 50
 
6.2%
] 50
 
6.2%
48
 
5.9%
a 43
 
5.3%
i 33
 
4.1%
l 31
 
3.8%
r 28
 
3.4%
n 28
 
3.4%
Other values (58) 326
40.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 403
49.6%
Other Punctuation 140
 
17.2%
Uppercase Letter 113
 
13.9%
Open Punctuation 50
 
6.2%
Close Punctuation 50
 
6.2%
Space Separator 48
 
5.9%
Decimal Number 9
 
1.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 50
 
12.4%
a 43
 
10.7%
i 33
 
8.2%
l 31
 
7.7%
r 28
 
6.9%
n 28
 
6.9%
u 26
 
6.5%
t 13
 
3.2%
k 13
 
3.2%
m 13
 
3.2%
Other values (20) 125
31.0%
Uppercase Letter
ValueCountFrequency (%)
B 14
 
12.4%
M 13
 
11.5%
K 11
 
9.7%
A 8
 
7.1%
S 8
 
7.1%
D 7
 
6.2%
I 4
 
3.5%
L 4
 
3.5%
E 4
 
3.5%
R 4
 
3.5%
Other values (17) 36
31.9%
Decimal Number
ValueCountFrequency (%)
3 3
33.3%
2 2
22.2%
8 2
22.2%
9 1
 
11.1%
4 1
 
11.1%
Other Punctuation
ValueCountFrequency (%)
' 126
90.0%
, 13
 
9.3%
& 1
 
0.7%
Open Punctuation
ValueCountFrequency (%)
[ 50
100.0%
Close Punctuation
ValueCountFrequency (%)
] 50
100.0%
Space Separator
ValueCountFrequency (%)
48
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 516
63.5%
Common 297
36.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 50
 
9.7%
a 43
 
8.3%
i 33
 
6.4%
l 31
 
6.0%
r 28
 
5.4%
n 28
 
5.4%
u 26
 
5.0%
B 14
 
2.7%
t 13
 
2.5%
k 13
 
2.5%
Other values (47) 237
45.9%
Common
ValueCountFrequency (%)
' 126
42.4%
[ 50
 
16.8%
] 50
 
16.8%
48
 
16.2%
, 13
 
4.4%
3 3
 
1.0%
2 2
 
0.7%
8 2
 
0.7%
9 1
 
0.3%
& 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 776
95.4%
None 37
 
4.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 126
16.2%
[ 50
 
6.4%
e 50
 
6.4%
] 50
 
6.4%
48
 
6.2%
a 43
 
5.5%
i 33
 
4.3%
l 31
 
4.0%
r 28
 
3.6%
n 28
 
3.6%
Other values (48) 289
37.2%
None
ValueCountFrequency (%)
ü 12
32.4%
ÅŸ 6
16.2%
ı 6
16.2%
ÄŸ 4
 
10.8%
Åž 3
 
8.1%
Ö 2
 
5.4%
İ 1
 
2.7%
ç 1
 
2.7%
ö 1
 
2.7%
Ç 1
 
2.7%

danceability
Real number (ℝ)

HIGH CORRELATION 

Distinct46
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6306
Minimum0.277
Maximum0.859
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2023-11-23T11:39:24.135441image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.277
5-th percentile0.383
Q10.55225
median0.6435
Q30.72625
95-th percentile0.8271
Maximum0.859
Range0.582
Interquartile range (IQR)0.174

Descriptive statistics

Standard deviation0.13636565
Coefficient of variation (CV)0.21624747
Kurtosis-0.16360271
Mean0.6306
Median Absolute Deviation (MAD)0.085
Skewness-0.52937435
Sum31.53
Variance0.018595592
MonotonicityNot monotonic
2023-11-23T11:39:24.371866image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0.724 2
 
4.0%
0.479 2
 
4.0%
0.356 2
 
4.0%
0.603 2
 
4.0%
0.776 1
 
2.0%
0.619 1
 
2.0%
0.727 1
 
2.0%
0.57 1
 
2.0%
0.549 1
 
2.0%
0.622 1
 
2.0%
Other values (36) 36
72.0%
ValueCountFrequency (%)
0.277 1
2.0%
0.356 2
4.0%
0.416 1
2.0%
0.44 1
2.0%
0.464 1
2.0%
0.471 1
2.0%
0.473 1
2.0%
0.479 2
4.0%
0.494 1
2.0%
0.544 1
2.0%
ValueCountFrequency (%)
0.859 1
2.0%
0.832 1
2.0%
0.828 1
2.0%
0.826 1
2.0%
0.822 1
2.0%
0.782 1
2.0%
0.776 1
2.0%
0.775 1
2.0%
0.77 1
2.0%
0.762 1
2.0%

energy
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.65478
Minimum0.372
Maximum0.905
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2023-11-23T11:39:24.636776image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.372
5-th percentile0.4568
Q10.529
median0.653
Q30.779
95-th percentile0.882
Maximum0.905
Range0.533
Interquartile range (IQR)0.25

Descriptive statistics

Standard deviation0.14348281
Coefficient of variation (CV)0.21913133
Kurtosis-1.0057398
Mean0.65478
Median Absolute Deviation (MAD)0.1265
Skewness0.037421695
Sum32.739
Variance0.020587318
MonotonicityNot monotonic
2023-11-23T11:39:24.875580image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0.78 2
 
4.0%
0.882 2
 
4.0%
0.516 1
 
2.0%
0.813 1
 
2.0%
0.619 1
 
2.0%
0.819 1
 
2.0%
0.502 1
 
2.0%
0.699 1
 
2.0%
0.816 1
 
2.0%
0.497 1
 
2.0%
Other values (38) 38
76.0%
ValueCountFrequency (%)
0.372 1
2.0%
0.39 1
2.0%
0.455 1
2.0%
0.459 1
2.0%
0.473 1
2.0%
0.487 1
2.0%
0.497 1
2.0%
0.502 1
2.0%
0.507 1
2.0%
0.515 1
2.0%
ValueCountFrequency (%)
0.905 1
2.0%
0.89 1
2.0%
0.882 2
4.0%
0.86 1
2.0%
0.859 1
2.0%
0.839 1
2.0%
0.819 1
2.0%
0.816 1
2.0%
0.813 1
2.0%
0.801 1
2.0%

key
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3
Minimum0
Maximum11
Zeros2
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2023-11-23T11:39:25.085467image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median7
Q39
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.3273756
Coefficient of variation (CV)0.52815486
Kurtosis-1.0108699
Mean6.3
Median Absolute Deviation (MAD)2.5
Skewness-0.3465872
Sum315
Variance11.071429
MonotonicityNot monotonic
2023-11-23T11:39:25.246919image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
9 8
16.0%
7 6
12.0%
1 5
10.0%
4 5
10.0%
11 5
10.0%
5 4
8.0%
8 4
8.0%
10 4
8.0%
3 3
 
6.0%
6 3
 
6.0%
Other values (2) 3
 
6.0%
ValueCountFrequency (%)
0 2
 
4.0%
1 5
10.0%
2 1
 
2.0%
3 3
 
6.0%
4 5
10.0%
5 4
8.0%
6 3
 
6.0%
7 6
12.0%
8 4
8.0%
9 8
16.0%
ValueCountFrequency (%)
11 5
10.0%
10 4
8.0%
9 8
16.0%
8 4
8.0%
7 6
12.0%
6 3
 
6.0%
5 4
8.0%
4 5
10.0%
3 3
 
6.0%
2 1
 
2.0%

loudness
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-7.88048
Minimum-14.11
Maximum-1.988
Zeros0
Zeros (%)0.0%
Negative50
Negative (%)100.0%
Memory size532.0 B
2023-11-23T11:39:25.443357image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-14.11
5-th percentile-11.52325
Q1-9.70225
median-7.696
Q3-6.1885
95-th percentile-3.9734
Maximum-1.988
Range12.122
Interquartile range (IQR)3.51375

Descriptive statistics

Standard deviation2.5566108
Coefficient of variation (CV)-0.32442324
Kurtosis-0.37676111
Mean-7.88048
Median Absolute Deviation (MAD)1.835
Skewness-0.095566992
Sum-394.024
Variance6.536259
MonotonicityNot monotonic
2023-11-23T11:39:25.713906image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-7.553 1
 
2.0%
-6.654 1
 
2.0%
-4.599 1
 
2.0%
-10.195 1
 
2.0%
-6.803 1
 
2.0%
-8.674 1
 
2.0%
-8.539 1
 
2.0%
-8.453 1
 
2.0%
-9.597 1
 
2.0%
-6.535 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
-14.11 1
2.0%
-12.12 1
2.0%
-11.557 1
2.0%
-11.482 1
2.0%
-11.477 1
2.0%
-11.335 1
2.0%
-11.052 1
2.0%
-10.798 1
2.0%
-10.431 1
2.0%
-10.389 1
2.0%
ValueCountFrequency (%)
-1.988 1
2.0%
-3.882 1
2.0%
-3.914 1
2.0%
-4.046 1
2.0%
-4.56 1
2.0%
-4.599 1
2.0%
-4.923 1
2.0%
-5.482 1
2.0%
-5.548 1
2.0%
-5.691 1
2.0%

mode
Categorical

Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
0
40 
1
10 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 40
80.0%
1 10
 
20.0%

Length

2023-11-23T11:39:25.935762image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-23T11:39:26.089057image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 40
80.0%
1 10
 
20.0%

Most occurring characters

ValueCountFrequency (%)
0 40
80.0%
1 10
 
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 50
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 40
80.0%
1 10
 
20.0%

Most occurring scripts

ValueCountFrequency (%)
Common 50
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 40
80.0%
1 10
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 40
80.0%
1 10
 
20.0%

speechiness
Real number (ℝ)

HIGH CORRELATION 

Distinct47
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.117494
Minimum0.0271
Maximum0.306
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2023-11-23T11:39:26.275643image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.0271
5-th percentile0.033595
Q10.046025
median0.08855
Q30.14
95-th percentile0.29465
Maximum0.306
Range0.2789
Interquartile range (IQR)0.093975

Descriptive statistics

Standard deviation0.083713502
Coefficient of variation (CV)0.71249172
Kurtosis-0.039028463
Mean0.117494
Median Absolute Deviation (MAD)0.0484
Skewness1.0167
Sum5.8747
Variance0.0070079504
MonotonicityNot monotonic
2023-11-23T11:39:26.504848image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0.0837 2
 
4.0%
0.14 2
 
4.0%
0.138 2
 
4.0%
0.27 1
 
2.0%
0.284 1
 
2.0%
0.134 1
 
2.0%
0.211 1
 
2.0%
0.0385 1
 
2.0%
0.0404 1
 
2.0%
0.205 1
 
2.0%
Other values (37) 37
74.0%
ValueCountFrequency (%)
0.0271 1
2.0%
0.0303 1
2.0%
0.0331 1
2.0%
0.0342 1
2.0%
0.0349 1
2.0%
0.0351 1
2.0%
0.0369 1
2.0%
0.0385 1
2.0%
0.0399 1
2.0%
0.0404 1
2.0%
ValueCountFrequency (%)
0.306 1
2.0%
0.301 1
2.0%
0.296 1
2.0%
0.293 1
2.0%
0.284 1
2.0%
0.27 1
2.0%
0.232 1
2.0%
0.212 1
2.0%
0.211 1
2.0%
0.205 1
2.0%

acousticness
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.278644
Minimum0.0056
Maximum0.835
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2023-11-23T11:39:26.766787image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.0056
5-th percentile0.020215
Q10.08625
median0.2225
Q30.44175
95-th percentile0.69815
Maximum0.835
Range0.8294
Interquartile range (IQR)0.3555

Descriptive statistics

Standard deviation0.23098348
Coefficient of variation (CV)0.82895552
Kurtosis-0.33776977
Mean0.278644
Median Absolute Deviation (MAD)0.1569
Skewness0.80738826
Sum13.9322
Variance0.053353369
MonotonicityNot monotonic
2023-11-23T11:39:26.994855image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.335 1
 
2.0%
0.143 1
 
2.0%
0.278 1
 
2.0%
0.279 1
 
2.0%
0.0199 1
 
2.0%
0.586 1
 
2.0%
0.102 1
 
2.0%
0.274 1
 
2.0%
0.16 1
 
2.0%
0.0372 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
0.0056 1
2.0%
0.012 1
2.0%
0.0199 1
2.0%
0.0206 1
2.0%
0.0256 1
2.0%
0.03 1
2.0%
0.0366 1
2.0%
0.0372 1
2.0%
0.059 1
2.0%
0.0629 1
2.0%
ValueCountFrequency (%)
0.835 1
2.0%
0.808 1
2.0%
0.713 1
2.0%
0.68 1
2.0%
0.673 1
2.0%
0.645 1
2.0%
0.586 1
2.0%
0.553 1
2.0%
0.533 1
2.0%
0.506 1
2.0%

instrumentalness
Real number (ℝ)

ZEROS 

Distinct28
Distinct (%)56.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0082650144
Minimum0
Maximum0.273
Zeros22
Zeros (%)44.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2023-11-23T11:39:27.196676image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.485 × 10-6
Q30.0002415
95-th percentile0.016805
Maximum0.273
Range0.273
Interquartile range (IQR)0.0002415

Descriptive statistics

Standard deviation0.039670033
Coefficient of variation (CV)4.7997537
Kurtosis42.712694
Mean0.0082650144
Median Absolute Deviation (MAD)2.485 × 10-6
Skewness6.3984781
Sum0.41325072
Variance0.0015737115
MonotonicityNot monotonic
2023-11-23T11:39:27.389126image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 22
44.0%
0.00508 2
 
4.0%
0.0136 1
 
2.0%
1.76 × 10-61
 
2.0%
0.000207 1
 
2.0%
4.32 × 10-61
 
2.0%
0.00232 1
 
2.0%
7.61 × 10-51
 
2.0%
0.00462 1
 
2.0%
0.00117 1
 
2.0%
Other values (18) 18
36.0%
ValueCountFrequency (%)
0 22
44.0%
1.2 × 10-61
 
2.0%
1.76 × 10-61
 
2.0%
1.95 × 10-61
 
2.0%
3.02 × 10-61
 
2.0%
3.48 × 10-61
 
2.0%
4.11 × 10-61
 
2.0%
4.32 × 10-61
 
2.0%
9.28 × 10-61
 
2.0%
1.1 × 10-51
 
2.0%
ValueCountFrequency (%)
0.273 1
2.0%
0.0719 1
2.0%
0.0173 1
2.0%
0.0162 1
2.0%
0.0136 1
2.0%
0.00508 2
4.0%
0.00462 1
2.0%
0.00232 1
2.0%
0.00124 1
2.0%
0.00117 1
2.0%

liveness
Real number (ℝ)

Distinct45
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.200952
Minimum0.0488
Maximum0.61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2023-11-23T11:39:27.606671image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.0488
5-th percentile0.07563
Q10.10875
median0.1435
Q30.256
95-th percentile0.4714
Maximum0.61
Range0.5612
Interquartile range (IQR)0.14725

Descriptive statistics

Standard deviation0.13315277
Coefficient of variation (CV)0.66260985
Kurtosis0.93259575
Mean0.200952
Median Absolute Deviation (MAD)0.05085
Skewness1.2965223
Sum10.0476
Variance0.017729661
MonotonicityNot monotonic
2023-11-23T11:39:27.879912image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0.121 2
 
4.0%
0.128 2
 
4.0%
0.475 2
 
4.0%
0.0953 2
 
4.0%
0.115 2
 
4.0%
0.09 1
 
2.0%
0.097 1
 
2.0%
0.117 1
 
2.0%
0.281 1
 
2.0%
0.61 1
 
2.0%
Other values (35) 35
70.0%
ValueCountFrequency (%)
0.0488 1
2.0%
0.0658 1
2.0%
0.0714 1
2.0%
0.0808 1
2.0%
0.0872 1
2.0%
0.0879 1
2.0%
0.09 1
2.0%
0.0953 2
4.0%
0.097 1
2.0%
0.0981 1
2.0%
ValueCountFrequency (%)
0.61 1
2.0%
0.475 2
4.0%
0.467 1
2.0%
0.437 1
2.0%
0.422 1
2.0%
0.421 1
2.0%
0.369 1
2.0%
0.296 1
2.0%
0.293 1
2.0%
0.289 1
2.0%

valence
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50022
Minimum0.143
Maximum0.919
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2023-11-23T11:39:28.112591image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.143
5-th percentile0.20325
Q10.3375
median0.4605
Q30.6785
95-th percentile0.864
Maximum0.919
Range0.776
Interquartile range (IQR)0.341

Descriptive statistics

Standard deviation0.21020887
Coefficient of variation (CV)0.42023283
Kurtosis-0.8934193
Mean0.50022
Median Absolute Deviation (MAD)0.166
Skewness0.26485102
Sum25.011
Variance0.044187767
MonotonicityNot monotonic
2023-11-23T11:39:28.356717image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.584 1
 
2.0%
0.323 1
 
2.0%
0.423 1
 
2.0%
0.446 1
 
2.0%
0.679 1
 
2.0%
0.746 1
 
2.0%
0.453 1
 
2.0%
0.713 1
 
2.0%
0.271 1
 
2.0%
0.853 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
0.143 1
2.0%
0.167 1
2.0%
0.192 1
2.0%
0.217 1
2.0%
0.228 1
2.0%
0.233 1
2.0%
0.245 1
2.0%
0.271 1
2.0%
0.272 1
2.0%
0.302 1
2.0%
ValueCountFrequency (%)
0.919 1
2.0%
0.91 1
2.0%
0.873 1
2.0%
0.853 1
2.0%
0.784 1
2.0%
0.775 1
2.0%
0.766 1
2.0%
0.746 1
2.0%
0.713 1
2.0%
0.71 1
2.0%

tempo
Real number (ℝ)

UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean124.91938
Minimum82.933
Maximum199.957
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2023-11-23T11:39:28.604917image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum82.933
5-th percentile92.0507
Q1103.341
median120.023
Q3141.71675
95-th percentile174.40535
Maximum199.957
Range117.024
Interquartile range (IQR)38.37575

Descriptive statistics

Standard deviation27.637967
Coefficient of variation (CV)0.22124643
Kurtosis0.078381233
Mean124.91938
Median Absolute Deviation (MAD)20.0415
Skewness0.7189312
Sum6245.969
Variance763.85723
MonotonicityNot monotonic
2023-11-23T11:39:28.896848image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
116.145 1
 
2.0%
177.995 1
 
2.0%
104.025 1
 
2.0%
97.99 1
 
2.0%
170.018 1
 
2.0%
160.009 1
 
2.0%
94.001 1
 
2.0%
142.002 1
 
2.0%
169.888 1
 
2.0%
157.954 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
82.933 1
2.0%
82.971 1
2.0%
91.004 1
2.0%
93.33 1
2.0%
93.892 1
2.0%
93.92 1
2.0%
94.001 1
2.0%
96.011 1
2.0%
97.99 1
2.0%
100.007 1
2.0%
ValueCountFrequency (%)
199.957 1
2.0%
188.011 1
2.0%
177.995 1
2.0%
170.018 1
2.0%
169.888 1
2.0%
160.009 1
2.0%
157.954 1
2.0%
154.949 1
2.0%
145.009 1
2.0%
144.033 1
2.0%

duration_ms
Real number (ℝ)

UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180945
Minimum122520
Maximum287226
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2023-11-23T11:39:29.158571image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum122520
5-th percentile131792.9
Q1152715
median170998
Q3202856.25
95-th percentile252547.2
Maximum287226
Range164706
Interquartile range (IQR)50141.25

Descriptive statistics

Standard deviation38570.795
Coefficient of variation (CV)0.21316309
Kurtosis0.13582825
Mean180945
Median Absolute Deviation (MAD)22549
Skewness0.78032794
Sum9047250
Variance1.4877062 × 109
MonotonicityNot monotonic
2023-11-23T11:39:29.410314image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
204413 1
 
2.0%
172154 1
 
2.0%
152375 1
 
2.0%
149388 1
 
2.0%
233573 1
 
2.0%
163022 1
 
2.0%
171383 1
 
2.0%
134049 1
 
2.0%
158174 1
 
2.0%
265800 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
122520 1
2.0%
124000 1
2.0%
129947 1
2.0%
134049 1
2.0%
134400 1
2.0%
138817 1
2.0%
144131 1
2.0%
146819 1
2.0%
147510 1
2.0%
149388 1
2.0%
ValueCountFrequency (%)
287226 1
2.0%
265800 1
2.0%
259704 1
2.0%
243800 1
2.0%
235800 1
2.0%
233573 1
2.0%
231727 1
2.0%
230162 1
2.0%
218710 1
2.0%
212062 1
2.0%

time_signature
Categorical

IMBALANCE 

Distinct3
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
4
48 
3
 
1
5
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)4.0%

Sample

1st row4
2nd row4
3rd row3
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 48
96.0%
3 1
 
2.0%
5 1
 
2.0%

Length

2023-11-23T11:39:29.630295image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-23T11:39:29.784331image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
4 48
96.0%
3 1
 
2.0%
5 1
 
2.0%

Most occurring characters

ValueCountFrequency (%)
4 48
96.0%
3 1
 
2.0%
5 1
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 50
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 48
96.0%
3 1
 
2.0%
5 1
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Common 50
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 48
96.0%
3 1
 
2.0%
5 1
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 48
96.0%
3 1
 
2.0%
5 1
 
2.0%

Interactions

2023-11-23T11:39:18.670867image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:02.669364image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:04.212395image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:05.708420image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:07.231121image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:08.822133image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:10.572577image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:12.077990image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:13.698140image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:15.280553image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:16.978671image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:18.813290image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:02.802218image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:04.347250image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:05.842404image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:07.377415image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:08.967492image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:10.707025image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:12.230408image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:13.835488image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:15.458303image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:17.116340image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:18.981761image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:02.952602image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:04.475594image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:05.985099image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:07.517772image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:09.101088image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:10.836399image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:12.377341image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:13.974356image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:15.650317image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:17.260780image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:19.136102image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:03.091647image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:04.605939image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:06.109472image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:07.665409image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:09.234270image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:10.980752image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:12.518966image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:14.103386image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:15.804196image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:17.445660image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:19.288096image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:03.229697image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:04.742059image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:06.247394image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:07.807147image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:09.613068image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:11.118075image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:12.676652image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:14.250389image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:15.960914image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:17.604078image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:19.437136image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:03.364950image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:04.872619image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:06.375099image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:07.952502image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:09.742415image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:11.247272image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:12.808574image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:14.398879image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:16.109522image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:17.758557image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:19.577922image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:03.488062image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:05.009205image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:06.499071image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:08.082963image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:09.868936image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:11.369306image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:12.940409image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:14.534378image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:16.249696image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:17.906294image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:19.726308image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:03.628132image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:05.142945image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:06.634623image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:08.224695image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:09.999730image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:11.501190image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:13.131647image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:14.671791image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:16.407130image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:18.080472image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:19.865912image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:03.767965image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:05.285064image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:06.764770image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:08.361726image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:10.142480image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:11.637115image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:13.267263image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:14.810198image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:16.546792image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:18.246364image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:20.021645image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:03.926969image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:05.424775image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:06.932126image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:08.518652image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:10.296063image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:11.794671image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:13.414808image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:14.959070image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:16.689080image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:18.389205image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:20.172114image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:04.064477image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:05.560361image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:07.079272image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:08.673527image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:10.436620image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:11.928184image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:13.554948image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:15.117186image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:16.831365image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T11:39:18.527252image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-11-23T11:39:29.897513image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
acousticnessdanceabilityduration_msenergyinstrumentalnesskeylivenessloudnessmodespeechinesstempotime_signaturevalence
acousticness1.000-0.1630.094-0.523-0.235-0.397-0.171-0.3590.0000.155-0.1340.000-0.065
danceability-0.1631.000-0.3330.329-0.074-0.0600.0090.2780.0000.5520.1440.4800.508
duration_ms0.094-0.3331.0000.0490.136-0.008-0.1540.0610.000-0.2840.0260.0000.000
energy-0.5230.3290.0491.0000.1860.2000.0800.6890.2030.2110.0180.1750.390
instrumentalness-0.235-0.0740.1360.1861.000-0.0010.026-0.0580.215-0.1450.2160.0000.171
key-0.397-0.060-0.0080.200-0.0011.0000.2070.1610.000-0.110-0.0840.000-0.041
liveness-0.1710.009-0.1540.0800.0260.2071.000-0.0980.2710.0830.0040.000-0.040
loudness-0.3590.2780.0610.689-0.0580.161-0.0981.0000.0000.2630.0730.0000.196
mode0.0000.0000.0000.2030.2150.0000.2710.0001.0000.2750.0760.0000.208
speechiness0.1550.552-0.2840.211-0.145-0.1100.0830.2630.2751.0000.0550.0000.222
tempo-0.1340.1440.0260.0180.216-0.0840.0040.0730.0760.0551.0000.0000.212
time_signature0.0000.4800.0000.1750.0000.0000.0000.0000.0000.0000.0001.0000.097
valence-0.0650.5080.0000.3900.171-0.041-0.0400.1960.2080.2220.2120.0971.000

Missing values

2023-11-23T11:39:20.644674image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-23T11:39:21.021629image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Track_IDTrack_NameTrack_Artistsdanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempoduration_mstime_signature
01KGvrw4RWClq7yt42sGG1vMargiela['Ezhel']0.7760.6481-7.55310.27000.3350.0000000.12100.584116.1452044134
13auN910HHNYykQo8i9q5J1Ateşe Düştüm['Mert Demir']0.6570.3724-14.11000.04760.8350.0136000.09810.919188.0112301624
20JU2QfhTrgFsVq5yQ4YQluAğlama ben ağlarım['Canozan']0.4160.4739-9.72600.03510.5530.0000010.16800.167118.3541916953
32huuuyJHQ6KK6qzwvUlG3cİlle de Sen['Muti', 'Azer Bülbül']0.6700.7145-6.14610.06310.1190.0000000.20700.535141.0711468194
44rbXJipz3CaSQwtG3xXzzTFark Ettim['Semicenk']0.6780.74511-6.98600.10500.0810.0000000.10100.588106.0461554064
51PiyTtoNn0m4kfJWLfjHnyBertaraf['Canbay & Wolker', 'Heijan', 'Muti']0.7240.9058-6.31610.23200.0300.0001660.46700.710105.0431537354
61wIdx6xw7B01SnnJoamriLYüreğim Ağlar['Amo988']0.6650.5770-9.07410.06440.4380.0000000.42100.318109.9782030004
76kvf06eThDCrvYknMY32ToMINI TERORISTA['Jeff Redd']0.7820.5078-12.12000.08370.1090.0000000.17900.433100.0301344004
83wtOAcctakLBwmzbCkRSHRKehribar['Burak Bulut', 'Ebru YaÅŸar']0.4400.6562-5.54800.29600.5330.0000000.08720.472100.0071689754
93ZwRkdnTb4az8X5n4WXuHAaklına ben gelicem['BLOK3']0.2770.54610-10.79800.06200.1480.0000000.24400.217199.9571494064
Track_IDTrack_NameTrack_Artistsdanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempoduration_mstime_signature
40232LbugfZaCUwq82xiELtdBAYBAY['BLOK3']0.7240.5693-10.43100.13500.21600.0023200.15200.775145.0091912764
415UV2oqgMXvlkOvvtK3aT7fBir Derdim Var['mor ve ötesi']0.5860.89011-1.98800.03690.01200.0050800.11400.426119.0622043204
420Y4mwQ4z0Voplgh7o2OZYmELLERİM BOŞ['M Lisa']0.6030.3904-9.46500.08770.71300.0000000.11500.272119.3921240004
4313THdxqHT11aSk3G8nUDzZAltüst Olmuşum['Mavi Gri']0.4790.5279-11.48200.02710.24700.0000040.24300.24582.9332024254
442PaQb8AtQqIxawPA2O7VawCelladına Aşık['Kubilay Karça']0.4940.6721-6.43200.04120.22800.0002070.09000.38696.0112015624
45006oGnrSZevqZTimsD2oh4Diva Yorgun['Melike Åžahin']0.6160.8825-6.56810.08940.32900.0000020.19100.639134.0462597044
4626h7iVBq3DSR9ex0BpT8GOLACIVERT['Motive', 'Pango']0.4640.4873-11.47700.08360.80800.0008260.10800.302109.4232358004
473miwaS9uZ7pZHi1zNyLGi0Tövbe['Derya Bedavacı']0.6750.7399-4.92300.07170.21700.0000000.28100.421141.9321921674
481G4xPTSdWG5oRdmmnOC7cSHoşçakal['Ati242']0.7500.7807-5.69100.21200.03660.0050800.47500.367140.0901909774
496iDXzCkxKklISwvWbPz8LvHele Bi (Club Version)['AliÅŸan']0.7700.70611-3.88200.08040.02560.0000000.04880.873105.0132438004